Modeling the ranking process in the neural network classifier of objects

As part of expert systems for various purposes, one of the basic ones is the decision support subsystem, which, in turn, requires the need for a procedure for classifying ob-jects. This is especially evident in intelligent medical diagnostic systems, which widely use artificial intelligence methods and tools. In this context, an approach involving mod-ern neurotechnology methods has proven to be effective at a high level. This paper con-siders a variant of the structural organization of a neural network classifier of objects as an improved model of the Hamming neural network. The peculiarity of this variant of the classifier is the expansion of its functionality by forming the ranks of the classified ob-ject in all defined classes. In the case of medical diagnosis, this means ranking all possi-ble diagnoses of a disease, i.e. determining not only the most likely diagnosis, but also the closest in rank to it. In fact, this will allow us to clarify the diagnosis, and thus im-prove the results of medical diagnosis. Accordingly, we simulated the classification pro-cess with the ranking of results, which corresponds to the classification with the realiza-tion of competition between the neurons of the competitive layer using negative-reverse (lateral) connections. This approach is basic in the theory of neural networks for deter-mining the winning neuron according to the WTA (Winner Takes All). Simulation model-ing of the classification variant was performed using specific biomedical data (eight symptoms) for the diagnosis of appendicitis (four diagnoses). The results of modeling the processes of neural network classification of objects with the formation of appropri-ate ranks are presented in the form of a table. They confirmed the correctness of the functioning algorithm for the considered classification model.